指令微调预训练因果语言模型以用于希腊纸草学与碑铭学
Instruct-Tuning Pretrained Causal Language Models for Ancient Greek Papyrology and Epigraphy
AI总结：
本文通过指令微调Llama 3.1模型，实现了古希腊纸草与铭文的文本复原、地理与年代归属，在文本复原任务上超越SOTA模型Ithaca，证实了该方法在古代文本校勘中的潜力。
摘要：
本文提出了一项微调预训练因果语言模型（Meta的Llama 3.1 8B Instruct）的实验，旨在协助复原古希腊铭文和文献纸草中缺失或难以辨认的字符。采用简单的基于指令的方法和95%/5%的训练/测试划分，纸草复原模型在长达10个字符的序列上实现了14.9%的字符错误率（CER）、73.5%的top-1准确率和86.0%的top-20准确率。此外还微调了一个用于地理归属的模型，达到66.4%的top-1准确率和79.9%的top-3准确率。在年代归属方面，其与实际terminus post/antequem的平均偏差为21.7年，中位数偏差为0年。对于铭文，复原模型在长达10个字符的序列上实现了20.5%的CER、63.7%的top-1准确率和83.0%的top-20准确率。在地理归属上，获得了75.0%的top-1准确率和83.7%的top-3准确率；在年代测定上，与实际日期范围的平均偏差为37.1年，中位数偏差为3年。在共享测试集和最新编辑的铭文上与最先进模型（Ithaca）进行基准测试，指令微调模型在文本复原方面表现卓越，同时提供了在重建期间忽略空格的实际优势，这与古代文本文物的scriptio continua（连续书写）特性相符。然而，它们在地理和年代归属上的表现低于Ithaca。为了在更均衡的设置下评估该方法，使用80%/10%/10%的训练-验证-测试划分重新训练了指令模型，在文本复原上依然优于Ithaca。结果表明，使用指令模板微调较大的预训练因果语言模型以对古代文本进行校勘和推测具有广阔前景。
英文摘要：
This article presents an experiment in fine-tuning a pretrained causal language model (Meta's Llama 3.1 8B Instruct) to assist with restoring missing or illegible characters in ancient Greek inscriptions and documentary papyri. Utilizing a straightforward instruction-based approach and a 95%/5% train/test split, the papyrus restoration model achieved a character error rate (CER) of 14.9%, a top-1 accuracy of 73.5%, and a top-20 accuracy of 86.0% for sequences up to 10 characters. A model was also fine-tuned for geographic attribution, reaching a top-1 accuracy of 66.4% and a top-3 accuracy of 79.9%. In chronological attribution, it demonstrated an average deviation of 21.7 years from the actual terminus post/ante quem, with a median deviation of 0 years. For inscriptions, the restoration model achieved a CER of 20.5%, a top-1 accuracy of 63.7%, and a top-20 accuracy of 83.0% for sequences up to 10 characters. In geographic attribution, it attained a top-1 accuracy of 75.0% and a top-3 accuracy of 83.7%, while in dating, it had an average deviation of 37.1 years and a median deviation of 3 years from the actual date range. Benchmarked against the state-of-the-art model (Ithaca) on a shared test set and on recently edited inscriptions, the instruction-tuned models excelled in text restoration, while also offering the practical advantage of ignoring spaces during reconstruction, which aligns with the scriptio continua of ancient textual artifacts. However, their performance in geographic and chronological attribution was lower than Ithaca's. To evaluate the approach in a more even setup, the instruction model was retrained with an 80%/10%/10% train-validation-test split, and still outperformed Ithaca in text restoration. The results suggest that fine-tuning larger pretrained causal language models using instruction templates for emendations and conjectures to ancient texts holds promise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
